👤 Guodong Huang

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1004
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Also published as: Ai-Chun Huang, Ai-long Huang, Aijie Huang, Ailong Huang, Aimin Huang, Alden Y Huang, An-Fang Huang, Annie Huang, Aohuan Huang, Ariane Huang, Baihai Huang, Baisong Huang, Bao-Hua Huang, Bao-Yi Huang, Baoqin Huang, Baoying Huang, Benjamin J Huang, Benlin Huang, Bevan E Huang, Bi Huang, Biao Huang, Bin Huang, Binfang Huang, Bing Huang, Bingcang Huang, Bingkun Huang, Bizhi Huang, Bo Huang, Bo-Shih Huang, Bor-Ren Huang, Bowen Huang, Boyue Huang, C Y Huang, Caihong Huang, Caiyun Huang, Can Huang, Canhua Huang, Caoxin Huang, Cathelin Huang, Catherine Huang, Chang Ming Huang, Chang X Huang, Chang-Jen Huang, Changjiang Huang, Chao Huang, Chao Wei Huang, Chao-Wei Huang, Chao-Yuan Huang, Chaolin Huang, Chaoqun Huang, Chaowang Huang, Chaoyang Huang, Chen Huang, Chen-Na Huang, Chen-Ping Huang, Cheng Huang, Chengcheng Huang, Chengrui Huang, Chenshen Huang, Chenxiao Huang, Chi-Cheng Huang, Chi-Shuan Huang, Chia-Chang Huang, Chia-Wei Huang, Chieh-Cheng Huang, Chieh-Liang Huang, Chien-Hsun Huang, Chih-Chun Huang, Chih-Hsiang Huang, Chih-Jen Huang, Chih-Ting Huang, Chih-Yang Huang, Chin-Chang Huang, Chin-Chou Huang, Ching-Shan Huang, Ching-Shin Huang, Ching-Tang Huang, Ching-Wei Huang, Chiu-Ju Huang, Chiu-Jung Huang, Chiun-Sheng Huang, Chong Huang, Chongbiao Huang, Christine S Huang, Chuan Huang, Chuanbing Huang, Chuanhong Huang, Chuanjiang Huang, Chuanjun Huang, Chuansheng Huang, Chuiguo Huang, Chun Huang, Chun-Mei Huang, Chun-Yao Huang, Chun-Yin Huang, Chunfan Huang, Chung-Hsiung Huang, Chunhong Huang, Chunjian Huang, Chunkai Huang, Chunlan Huang, Chunling Huang, Chunshuai Huang, Chunxia Huang, Chunyao Huang, Chunyi Huang, Chunying Huang, Chunyu Huang, Chuxin Huang, Chuying Huang, Congcong Huang, Cuiyu Huang, Da Huang, Dajun Huang, Dan Huang, Dane Huang, Danqing Huang, Dantong Huang, David Huang, David J Huang, De Huang, De-Jun Huang, Dejia Huang, Dengjun Huang, Dianhua Huang, Dishu Huang, Dong Huang, Donglan Huang, Dongmei Huang, Dongni Huang, Dongqin Huang, Dongqing Huang, Dongsheng Huang, Dongyu Huang, Du-Juan Huang, Emily C Huang, Enhao Huang, Enping Huang, Eric Huang, Erya Huang, F Huang, Fan Huang, Fang Huang, Fang-Ling Huang, Fangling Huang, Fei Huang, Fei Wan Huang, Feiruo Huang, Feiteng Huang, Feizhou Huang, Feng Huang, Fengxian Huang, Fengyu Huang, Franklin W Huang, Fu-Chen Huang, Fu-Mei Huang, Fubiao Huang, Fude Huang, Fuhao Huang, Furong Huang, G Huang, Gairong Huang, Gang Huang, Gao-Zhong Huang, Gaoxingyu Huang, Ge Huang, Guang-Jian Huang, Guang-Yun Huang, Guangjian Huang, Guangming Huang, Guangqian Huang, Guangrui Huang, Guanhong Huang, Guanling Huang, Guanning Huang, Guanqun Huang, Guanrong Huang, Guicheng Huang, Guohong Huang, Guoping Huang, Guoqian Huang, Guowei Huang, Guoxing Huang, Guoying Huang, Guoyong Huang, Guoyuan Huang, H Huang, H S Huang, Hai Huang, Haigang Huang, Haihong Huang, Hailin Huang, Haimiao Huang, Haixin Huang, Haiyan Huang, Han-Chang Huang, Hanxia Huang, Hao Huang, Hao-Fei Huang, Haobo Huang, Haochu Huang, Haomin Huang, Haoyu Huang, Haoyue Huang, Haozhang Huang, Haozhong Huang, He Huang, Hefeng Huang, Heguang Huang, Helen Huang, Heming Huang, Hengbin Huang, Heqing Huang, Hete Huang, Hong Huang, Hongbiao Huang, Hongcan Huang, Hongda Huang, Hongfei Huang, Hongfeng Huang, Honghui Huang, Hongou Huang, Hongqiang Huang, Hongyan Huang, Hongyang Huang, Hongyi Huang, Hongying Huang, Hongyu Huang, Hongyun Huang, Hsi-Yuan Huang, Hsien-Da Huang, Hsing-Yen Huang, Hsu Chih Huang, Hsuan-Cheng Huang, Hsuan-Ying Huang, Hu Huang, Hua Huang, Huafei Huang, Huaju Huang, Huan Huang, Huanhuan Huang, Huanliang Huang, Huapin Huang, Huashan Huang, Huayun Huang, Hui Huang, Hui-Huang Huang, Hui-Kuang Huang, Hui-Yu Huang, Huibin Huang, Huifen Huang, Huiling Huang, Huimin Huang, Huina Huang, Huiqiao Huang, Huixian Huang, Huixin Huang, Huiyan Huang, Huiyu Huang, Huizhe Huang, Huizhen Huang, Hy Huang, I-Chieh Huang, J V Huang, Janice J Huang, Jasmin Huang, Jeffrey K Huang, Jia Huang, Jia-Jia Huang, Jiaan Huang, Jiahui Huang, Jiajin Huang, Jiajun Huang, Jian Huang, Jian-Dong Huang, Jiana Huang, Jianbiao Huang, Jianbing Huang, Jianfang Huang, Jianfeng Huang, Jiangfeng Huang, Jiangtao Huang, Jiangwei Huang, Jianhua Huang, Jianlu Huang, Jianmin Huang, Jianming Huang, Jiansheng Huang, Jianzhen Huang, Jiao-Qian Huang, Jiaoti Huang, Jiaotian Huang, Jiaqi Huang, Jiawen Huang, Jiaxing Huang, Jiayu Huang, Jiayue Huang, Jie Huang, Jie Qi Huang, Jiechun Huang, Jieli Huang, Jieling Huang, Jieping Huang, Jin Huang, Jin-Di Huang, Jin-Feng Huang, Jin-Hong Huang, Jin-Yan Huang, Jinbao Huang, Jinfang Huang, Jing Huang, Jing-Fei Huang, Jingang Huang, Jinghan Huang, Jingjing Huang, Jingkun Huang, Jinglong Huang, Jingtao Huang, Jingxian Huang, Jingyong Huang, Jingyuan Huang, Jingyue Huang, Jinhua Huang, Jinling Huang, Jinlu Huang, Jinshu Huang, Jinxing Huang, Jinyan Huang, Jinzhou Huang, Jiuhong Huang, Jiyu Huang, Ju Huang, Juan Huang, Jucun Huang, Jun Huang, Jun-Hua Huang, Jun-You Huang, Junhao Huang, Junhua Huang, Junjie Huang, Junming Huang, Junning Huang, Junqi Huang, Junwen Huang, Junyuan Huang, Junyun Huang, Juxiang Huang, K Huang, K N Huang, Kai Huang, Kaipeng Huang, Kang Huang, Kangbo Huang, Kate Huang, Katherine Huang, Ke Huang, Ke-Ke Huang, Ke-Pu Huang, Kevin Huang, Kevin Y Huang, Kuan-Chun Huang, Kui-Yuan Huang, Kuiyuan Huang, Kun Huang, Kuo-Hsiang Huang, Kuo-Hung Huang, L Huang, L-B Huang, Laiqiang Huang, Lan Huang, Lanlan Huang, Lei Huang, Leijuan Huang, Li Huang, Li-Hao Huang, Li-Jiang Huang, Li-Juan Huang, Li-Jun Huang, Li-Ping Huang, Li-Rung Huang, Li-Wei Huang, Li-Yun Huang, Lian Huang, Liang Huang, Liang-Yu Huang, Liangchong Huang, Lianggui Huang, Libin Huang, Lige Huang, Lihua Huang, Lijia Huang, Lijiang Huang, Lijuan Huang, Lijun Huang, Lili Huang, Limin Huang, Liming Huang, Lin Huang, Linchen Huang, Ling Huang, Ling-Chun Huang, Ling-Jin Huang, Lingling Huang, Lining Huang, Linjing Huang, Linsheng Huang, Linxue Huang, Linyuan Huang, Liping Huang, Liqiong Huang, Lixia Huang, Lixiang Huang, Lixuan Huang, Lixue Huang, Lizhen Huang, Longfei Huang, Lu Huang, Lu-Jie Huang, Lu-Qi Huang, Luanluan Huang, Luqi Huang, Luyang Huang, Luyao Huang, Lvzhen Huang, M C Huang, Man Huang, Manning Y Huang, Manyun Huang, Mao-Mao Huang, Mei Huang, Meihua Huang, Meina Huang, Meixiang Huang, Melissa Y Huang, Meng-Chuan Huang, Meng-Fan Huang, Meng-Na Huang, MengQian Huang, Menghao Huang, Mengjie Huang, Mengjun Huang, Mengnan Huang, Mengting Huang, Mengzhen Huang, Mia L Huang, Miao Huang, Min Huang, Ming-Lu Huang, Ming-Shyan Huang, Mingjian Huang, Mingjun Huang, Minglei Huang, Mingrui Huang, Mingwei Huang, Mingxuan Huang, Mingyu Huang, Mingyuan Huang, Minjun Huang, Minqi Huang, Minxuan Huang, Minyuan Huang, N Huang, Na Huang, Nian Huang, Nianyuan Huang, Ning-Na Huang, Ning-Ping Huang, Ninghao Huang, Nongyu Huang, Pan Huang, Pang-Shuo Huang, Paul L Huang, Pei Huang, Pei-Chi Huang, Pei-Ying Huang, Peiying Huang, Peng Huang, Peng-Fei Huang, Pengyu Huang, Piao-Piao Huang, Piaopiao Huang, Pin-Rui Huang, Ping Huang, Pingping Huang, Pintong Huang, Po-Hsun Huang, Po-Jung Huang, Poyao Huang, Qi Huang, Qi-Tao Huang, Qian Huang, Qiang Huang, Qianqian Huang, Qiaobing Huang, Qibin Huang, Qidi Huang, Qin Huang, Qing Huang, Qing-yong Huang, Qingjiang Huang, Qingke Huang, Qingling Huang, Qingqing Huang, Qingsong Huang, Qingxia Huang, Qingxing Huang, Qingyu Huang, Qingzhi Huang, Qinlou Huang, Qiong Huang, Qiubo Huang, Qiumin Huang, Qiuming Huang, Qiuru Huang, Qiuyin Huang, Qiuyue Huang, Qizhen Huang, Quanfang Huang, Qun Huang, R H Huang, R Stephanie Huang, Rae-Chi Huang, Ran Huang, Renbin Huang, Renhua Huang, Renli Huang, Richard Huang, Richard S P Huang, Riqing Huang, Ritai Huang, Robert J Huang, Rong Huang, Rong Stephanie Huang, Ronghua Huang, Ronghui Huang, Rongjie Huang, Rongrong Huang, Rongxiang Huang, Ru-Ting Huang, Ruby Yun-Ju Huang, Rui Huang, Ruihua Huang, Ruijin Huang, Ruina Huang, Ruiyan Huang, Ruizhen Huang, Runyue Huang, Ruo-Hui Huang, S Huang, S Y Huang, S Z Huang, Saisai Huang, San-Yuan Huang, See-Chang Huang, Sen Huang, Serina Huang, Shan Huang, Shang-Ming Huang, Shanhe Huang, Shanshan Huang, Shaojun Huang, Shaoxin Huang, Shaoze Huang, Shau Ku Huang, Shau-Ku Huang, Shenan Huang, Sheng-He Huang, Shengfeng Huang, Shengjie Huang, Shengnan Huang, Shengyan Huang, Shengyun Huang, Shi-Feng Huang, Shi-Shi Huang, Shi-Ying Huang, Shiang-Suo Huang, Shichao Huang, Shih-Chiang Huang, Shih-Wei Huang, Shih-Yi Huang, Shihao Huang, Shijing Huang, Shilu Huang, Shixia Huang, Shiya Huang, Shiying Huang, Shiyun Huang, Shoucheng Huang, Shu Huang, Shu-Pang Huang, Shu-Pin Huang, Shu-Qiong Huang, Shu-Wei Huang, Shu-Yi Huang, Shu-ying Huang, Shuai Huang, Shuang Huang, Shungen Huang, Shuo Huang, Shushu Huang, Shutong Huang, Shuwen Huang, Si-Yang Huang, Sidong Huang, Sihua Huang, Sijia Huang, Sinchun Huang, Sisi Huang, Sixiu Huang, Song Bin Huang, Song-Mei Huang, Songmei Huang, Songming Huang, Songqian Huang, Steven Huang, Steven Kuan-Hua Huang, Suli Huang, Sung-Ying Huang, Susan M Huang, Suwen Huang, Taiqi Huang, Tang-Hsiu Huang, Tao Huang, Te-Hsuan Huang, Tengda Huang, Tengfei Huang, Tian Hao Huang, Tianhao Huang, Tianpu Huang, Tiantian Huang, Tieqiu Huang, Tim H Huang, Ting Huang, Tinghua Huang, Tingping Huang, Tingqin Huang, Tingting Huang, Tingxuan Huang, Tingyun Huang, Tong Huang, Tongsheng Huang, Tongtong Huang, Tony T Huang, Tse-Shun Huang, Tseng-Yu Huang, Tsung-Wei Huang, Tzu-Rung Huang, Wan-Ping Huang, Way-Ren Huang, Wei Huang, Wei-Chi Huang, Weibin Huang, Weicheng Huang, Weifeng Huang, Weihua Huang, Weijun Huang, Weiqi Huang, Weisu Huang, Weiwei Huang, Weixue Huang, Weizhen Huang, Wen Huang, Wen-yu Huang, Wenbin Huang, Wenda Huang, Wenfang Huang, Wenfeng Huang, Wenhua Huang, Wenji Huang, Wenjie Huang, Wenjun Huang, Wenqiao Huang, Wenqing Huang, Wenqiong Huang, Wenshan Huang, Wentao Huang, Wenxin Huang, Wenya Huang, Wenying Huang, Wunan Huang, Wuqing Huang, X F Huang, X Huang, Xi Huang, Xian-sheng HUANG, Xiang Huang, Xianghua Huang, Xianglong Huang, Xiangming Huang, Xianping Huang, Xianqing Huang, Xiansheng Huang, Xianwei Huang, Xianxi Huang, Xianxian Huang, Xianying Huang, Xianzhang Huang, Xiao Huang, Xiao-Fang Huang, Xiao-Fei Huang, Xiao-Ming Huang, Xiao-Song Huang, Xiao-Yan Huang, Xiao-Yong Huang, Xiao-Yu Huang, XiaoFang Huang, Xiaochun Huang, Xiaofei Huang, Xiaofeng Huang, Xiaohong Huang, Xiaohua Huang, Xiaojie Huang, Xiaojing Huang, Xiaojuan Huang, Xiaolan Huang, Xiaoli Huang, Xiaolin Huang, Xiaoman Huang, Xiaomin Huang, Xiaoqing Huang, Xiaoshuai Huang, Xiaowen Huang, Xiaowu Huang, Xiaoxia Huang, Xiaoyan Huang, Xiaoying Huang, Xiaoyu Huang, Xiaoyuan Huang, Xiaoyun Huang, Xiaozhun Huang, Xiayang Huang, Xichang Huang, Xie-Lin Huang, Xin Huang, Xin-Di Huang, Xinen Huang, Xinfeng Huang, Xingguo Huang, Xingming Huang, Xingqin Huang, Xingru Huang, Xingxu Huang, Xingya Huang, Xingzhen Huang, Xinwen Huang, Xinyi Huang, Xinying Huang, Xinyue Huang, Xinzhu Huang, Xiongfeng Huang, Xionggao Huang, Xiuju Huang, Xiuyun Huang, Xiuzhen Huang, Xiwen Huang, Xu Huang, Xu-Feng Huang, Xuan Huang, Xuanzhang Huang, Xucong Huang, Xudong Huang, Xue-Ying Huang, Xue-shuang Huang, Xuehong Huang, Xuejie Huang, Xuejing Huang, Xuejun Huang, Xuemei Huang, Xueming Huang, Xueqi Huang, Xuewei Huang, Xuezhe Huang, Xuhui Huang, Xuliang Huang, Xun Huang, Xuxiong Huang, Y Huang, Y Joyce Huang, Y S Huang, Ya-Chih Huang, Ya-Dong Huang, Ya-Fang Huang, Ya-Ru Huang, Yabo Huang, Yadong Huang, Yafang Huang, Yajiao Huang, Yajuan Huang, Yali Huang, Yamei Huang, Yan Huang, Yan-Lin Huang, Yan-Qing Huang, Yan-Ting Huang, Yang Huang, Yang Zhong Huang, Yangqing Huang, Yangyang Huang, Yanhao Huang, Yani Huang, Yanjun Huang, Yanlong Huang, Yanna Huang, Yanping Huang, Yanqin Huang, Yanqing Huang, Yanqun Huang, Yanru Huang, Yanshan Huang, Yansheng Huang, Yanxia Huang, Yanyan Huang, Yanyao Huang, Yao Huang, Yao-Kuang Huang, Yaowei Huang, Yatian Huang, Yating Huang, Ye Huang, Yechao Huang, Yen-Chu Huang, Yen-Ning Huang, Yen-Tsung Huang, Yeqing Huang, Yewei Huang, Yi Huang, Yi-Chun Huang, Yi-Jan Huang, Yi-Jia Huang, Yi-Wen Huang, Yi-ping Huang, Yichao Huang, Yichuan Huang, Yicong Huang, Yifan Huang, Yihao Huang, Yiheng Huang, Yihong Huang, Yikeng Huang, Yilin Huang, Yin Huang, Yin-Tsen Huang, Ying Huang, Ying-Hsuan Huang, Ying-Jung Huang, Ying-Zhi Huang, Yinghua Huang, Yingying Huang, Yingzhen Huang, Yingzhi Huang, Yiping Huang, Yiquan Huang, Yishan Huang, Yiwei Huang, Yixian Huang, Yizhou Huang, Yong Huang, Yong-Fu Huang, Yongbiao Huang, Yongcan Huang, Yongjie Huang, Yongqi Huang, Yongsheng Huang, Yongtong Huang, Yongye Huang, Yongyi Huang, Yongzhen Huang, Youheng Huang, Youyang Huang, Yu Huang, Yu-Ching Huang, Yu-Chu Huang, Yu-Chuen Huang, Yu-Chyi Huang, Yu-Fang Huang, Yu-Han Huang, Yu-Jie Huang, Yu-Lei Huang, Yu-Ren Huang, Yu-Shu Huang, Yu-Ting Huang, Yuan Huang, Yuan-Lan Huang, Yuan-Li Huang, Yuan-Lu Huang, Yuancheng Huang, Yuanpeng Huang, Yuanshuai Huang, Yuanyu Huang, Yuanyuan Huang, Yue Huang, Yue-Hua Huang, Yuedi Huang, Yueh-Hsiang Huang, Yuehong Huang, Yuejun Huang, Yueye Huang, Yuezhen Huang, Yufang Huang, Yufen Huang, Yuguang Huang, Yuh-Chin T Huang, Yuhong Huang, Yuhua Huang, Yuhui Huang, Yujia Huang, Yujie Huang, Yulin Huang, Yumei Huang, Yumeng Huang, Yun Huang, Yun-Juan Huang, Yunchao Huang, Yung-Hsin Huang, Yung-Yu Huang, Yunmao Huang, Yunpeng Huang, Yunru Huang, Yunyan Huang, Yuping Huang, Yuqi Huang, Yuqiang Huang, Yuqiong Huang, Yusi Huang, Yutang Huang, Yuting Huang, Yutong Huang, Yuxian Huang, Yuxin Huang, Yuxuan Huang, Yuyang Huang, Yuying Huang, Z Huang, Z Z Huang, Z-Y Huang, Zebin Huang, Zebo Huang, Zehua Huang, Zeling Huang, Zengwen Huang, Zhang Huang, Zhao Huang, Zhaoxia Huang, Zhe Huang, Zhen Huang, Zhenfei Huang, Zheng Huang, Zheng-Xiang Huang, Zhengwei Huang, Zhengxian Huang, Zhengxiang Huang, Zhengyang Huang, Zhenlin Huang, Zhenrui Huang, Zhenyao Huang, Zhenyi Huang, Zhi Huang, Zhi-Ming Huang, Zhi-Qiang Huang, Zhi-Xin Huang, Zhi-xiang Huang, Zhican Huang, Zhicong Huang, Zhifang Huang, Zhifeng Huang, Zhigang Huang, Zhihong Huang, Zhilin Huang, Zhilong Huang, Zhipeng Huang, Zhiping Huang, Zhiqi Huang, Zhiqiang Huang, Zhiqin Huang, Zhiqing Huang, Zhitong Huang, Zhiwei Huang, Zhixiang Huang, Zhiying Huang, Zhiyong Huang, Zhiyu Huang, Zhongbin Huang, Zhongcheng Huang, Zhongfeng Huang, Zhonglu Huang, Zhouyang Huang, Zi-Xin Huang, Zi-Ye Huang, Zicheng Huang, Zichong Huang, Zihan Huang, Zihao Huang, Ziheng Huang, Ziling Huang, Zini Huang, Zirui Huang, Zizhan Huang, Zongjian Huang, Zongliang Huang, Zunnan Huang, Zuotian Huang, Zuxian Huang, Zuyi Huang
articles

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Yeak-Wun Quek, Yu-Ting Kang, Hsu Chih Huang +4 more · 2024 · Mutation research · Elsevier · added 2026-04-24
Fine particulate matter (PM
no PDF DOI: 10.1016/j.mrfmmm.2024.111887
ANGPTL4
Bin Fang, Rou Mo, Xing Lin +2 more · 2024 · Phytomedicine : international journal of phytotherapy and phytopharmacology · Elsevier · added 2026-04-24
Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases accompanied by lipid and glucose metabolism disorder. Didymin has been reported to have various hepatoprotective effe Show more
Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases accompanied by lipid and glucose metabolism disorder. Didymin has been reported to have various hepatoprotective effects, however, its potential effects and mechanisms on NAFLD remain unclear from the perspective of the whole. To investigate the underlying mechanism of didymin against NAFLD using multi-omics technologies. Rats were fed with a high-fat diet (HFD) for 8 weeks to induce NAFLD, followed by didymin treatment for 8 weeks. Next, biochemical analysis and histopathological examinations were performed to evaluate the effects of didymin. The key regulating pathways were predicted using transcriptomics, metabolomics and proteomics, and the target pathways were then verified by detecting the key genes/proteins using various experiments. Didymin markedly mitigated liver injury and excessive lipid droplet accretion. An integrative multi-omics analysis suggested that the PPAR signaling cascade and insulin signaling pathway might serve as pivotal mechanisms underlying the modulation of lipid and glucose homeostasis by didymin. Further dissection identified five pivotal genes (PPARα, PPARβ, FABP4, ANGPTL4, and PLIN2) and four genes (HK1, HK3, GCK, and PTPN1) as potential hubs within these pathways. Subsequent validation experiments, including qPCR and Western blot, demonstrated upregulated expression of PPARα and PPARβ, indicating the activation of the PPAR pathway by didymin. Concurrently, didymin appeared to modulate the insulin signaling pathway, as evidenced by the upregulated expression of HK1 and downregulated expression of PTPN1. Notably, the manipulation of PPARα, PPARβ, and PTPN1 expression in LO2 cells through silence or overexpression confirmed that didymin significantly reduced lipid accumulation, with its molecular targets likely being the PPAR and insulin pathways. Our findings demonstrate that didymin has a protective effect on NAFLD, and its underlying mechanism may be associated with the regulation of the PPAR and insulin signaling pathways. Show less
no PDF DOI: 10.1016/j.phymed.2024.156016
ANGPTL4
Xiaomin Liu, Yiliang Zhang, Bingqian Han +10 more · 2024 · JCI insight · added 2026-04-24
Fuel substrate switching between carbohydrates and fat is essential for maintaining metabolic homeostasis. During aerobic exercise, the predominant energy source gradually shifts from carbohydrates to Show more
Fuel substrate switching between carbohydrates and fat is essential for maintaining metabolic homeostasis. During aerobic exercise, the predominant energy source gradually shifts from carbohydrates to fat. While it is well known that exercise mobilizes fat storage from adipose tissues, it remains largely obscure how circulating lipids are distributed tissue-specifically according to distinct energy requirements. Here, we demonstrate that aerobic exercise is linked to nutrient availability to regulate tissue-specific activities of lipoprotein lipase (LPL), the key enzyme catabolizing circulating triglyceride (TG) for tissue uptake, through the differential actions of angiopoietin-like (ANGPTL) proteins. Exercise reduced the tissue binding of ANGPTL3 protein, increasing LPL activity and TG uptake in the heart and skeletal muscle in the postprandial state specifically. Mechanistically, exercise suppressed insulin secretion, attenuating hepatic Angptl8 transcription through the PI3K/mTOR/CEBPα pathway, which is imperative for the tissue binding of its partner ANGPTL3. Constitutive expression of ANGPTL8 hampered lipid utilization and resulted in cardiac dysfunction in response to exercise. Conversely, exercise promoted the expression of ANGPTL4 in white adipose tissues, overriding the regulatory actions of ANGPTL8/ANGPTL3 in suppressing adipose LPL activity, thereby diverting circulating TG away from storage. Collectively, our findings show an overlooked bifurcated ANGPTL-LPL network that orchestrates fuel switching in response to aerobic exercise. Show less
📄 PDF DOI: 10.1172/jci.insight.181553
ANGPTL4
Seyedeh Fatemeh Razavipour, Hyunho Yoon, Kibeom Jang +13 more · 2024 · Nature communications · Nature · added 2026-04-24
In many cancers, a stem-like cell subpopulation mediates tumor initiation, dissemination and drug resistance. Here, we report that cancer stem cell (CSC) abundance is transcriptionally regulated by C- Show more
In many cancers, a stem-like cell subpopulation mediates tumor initiation, dissemination and drug resistance. Here, we report that cancer stem cell (CSC) abundance is transcriptionally regulated by C-terminally phosphorylated p27 (p27pT157pT198). Mechanistically, this arises through p27 co-recruitment with STAT3/CBP to gene regulators of CSC self-renewal including MYC, the Notch ligand JAG1, and ANGPTL4. p27pTpT/STAT3 also recruits a SIN3A/HDAC1 complex to co-repress the Pyk2 inhibitor, PTPN12. Pyk2, in turn, activates STAT3, creating a feed-forward loop increasing stem-like properties in vitro and tumor-initiating stem cells in vivo. The p27-activated gene profile is over-represented in STAT3 activated human breast cancers. Furthermore, mammary transgenic expression of phosphomimetic, cyclin-CDK-binding defective p27 (p27CK-DD) increases mammary duct branching morphogenesis, yielding hyperplasia and microinvasive cancers that can metastasize to liver, further supporting a role for p27pTpT in CSC expansion. Thus, p27pTpT interacts with STAT3, driving transcriptional programs governing stem cell expansion or maintenance in normal and cancer tissues. Show less
📄 PDF DOI: 10.1038/s41467-024-48742-y
ANGPTL4
Wenlong Deng, Liang Zhong, Shupei Ye +4 more · 2024 · Journal of bioenergetics and biomembranes · Springer · added 2026-04-24
Ferritinophagy-mediated ferroptosis plays a crucial role in fighting pathogen aggression. The long non-coding RNA Mir22hg is involved in the regulation of ferroptosis and aberrantly overexpression in Show more
Ferritinophagy-mediated ferroptosis plays a crucial role in fighting pathogen aggression. The long non-coding RNA Mir22hg is involved in the regulation of ferroptosis and aberrantly overexpression in lipopolysaccharide (LPS)-induced sepsis mice, but whether it regulates sepsis through ferritinophagy-mediated ferroptosis is unclear. Mir22hg was screened by bioinformatics analysis. Ferroptosis was assessed by assaying malondialdehyde (MDA), reactive oxygen species (ROS), and Fe Mir22hg silencing lightened ferroptosis and ferritinophagy in LPS-induced MLE-12 cells and sepsis mouse models, as presented by the downregulated MDA, ROS, Fe Mir22hg contributed to in ferritinophagy-mediated ferroptosis in sepsis via recruiting the m6A reader YTHDC1 and strengthening Angptl4 mRNA stability, highlighting that Mir22hg may be a potential target for sepsis treatment based on ferroptosis. Show less
📄 PDF DOI: 10.1007/s10863-024-10022-1
ANGPTL4
Chaojun Zhu, Lan Teng, Yihong Lai +14 more · 2024 · Cellular and molecular life sciences : CMLS · Springer · added 2026-04-24
Peritoneal metastasis, the third most common metastasis in colorectal cancer (CRC), has a poor prognosis for the rapid progression and limited therapeutic strategy. However, the molecular characterist Show more
Peritoneal metastasis, the third most common metastasis in colorectal cancer (CRC), has a poor prognosis for the rapid progression and limited therapeutic strategy. However, the molecular characteristics and pathogenesis of CRC peritoneal metastasis are poorly understood. Here, we aimed to elucidate the action and mechanism of adipose-derived stem cells (ADSCs), a prominent component of the peritoneal microenvironment, in CRC peritoneal metastasis formation. Database analysis indicated that ADSCs infiltration was increased in CRC peritoneal metastases, and high expression levels of ADSCs marker genes predicted a poor prognosis. Then we investigated the effect of ADSCs on CRC cells in vitro and in vivo. The results revealed that CRC cells co-cultured with ADSCs exhibited stronger metastatic property and anoikis resistance, and ADSCs boosted the intraperitoneal seeding of CRC cells. Furthermore, RNA sequencing was carried out to identify the key target gene, angiopoietin like 4 (ANGPTL4), which was upregulated in CRC specimens, especially in peritoneal metastases. Mechanistically, TGF-β1 secreted by ADSCs activated SMAD3 in CRC cells, and chromatin immunoprecipitation assay showed that SMAD3 facilitated ANGPTL4 transcription by directly binding to ANGPTL4 promoter. The ANGPTL4 upregulation was essential for ADSCs to promote glycolysis and anoikis resistance in CRC. Importantly, simultaneously targeting TGF-β signaling and ANGPTL4 efficiently reduced intraperitoneal seeding in vivo. In conclusion, this study indicates that tumor-infiltrating ADSCs promote glycolysis and anoikis resistance in CRC cells and ultimately facilitate peritoneal metastasis via the TGF-β1/SMAD3/ANGPTL4 axis. The dual-targeting of TGF-β signaling and ANGPTL4 may be a feasible therapeutic strategy for CRC peritoneal metastasis. Show less
📄 PDF DOI: 10.1007/s00018-024-05215-1
ANGPTL4
Zhiyu Ma, Nana Wang, Tingting Meng +3 more · 2024 · Journal of biochemical and molecular toxicology · Wiley · added 2026-04-24
Recent studies have shown that epithelial-mesenchymal transition (EMT) plays an important role in paraquat (PQ)-induced tissue fibrosis, which is the main cause of death in patients with PQ poisoning. Show more
Recent studies have shown that epithelial-mesenchymal transition (EMT) plays an important role in paraquat (PQ)-induced tissue fibrosis, which is the main cause of death in patients with PQ poisoning. However, no effective treatment for pulmonary interstitial fibrosis caused by PQ poisoning exists. It is of great significance for us to find new therapeutic targets through bioinformatics in PQ-induced EMT. We conducted transcriptome sequencing to determine the expression profiles of 1210 messenger RNAs (mRNAs), 558 long noncoding RNAs, 28 microRNAs (miRNAs), including 18 known-miRNAs, 10 novel-miRNAs and 154 circular RNAs in the PQ-exposed EMT group mice. Using gene ontology and Kyoto Encyclopaedia of Genes and Genomes analyses, we identified the pathways associated with signal transduction, cancers, endocrine systems and immune systems were involved in PQ-induced EMT. Furthermore, we constructed long noncoding RNA-miRNA-mRNA interrelated networks and found that upregulated genes included Il22ra2, Mdm4, Slc35e2 and Angptl4, and downregulated genes included RGS2, Gabpb2, Acvr1, Prkd3, Sp100, Tlr12, Syt15 and Camk2d. Thirteen new potential competitive endogenous RNA targets were also identified for further treatment of PQ-induced pulmonary tissue fibrosis. Through further study of the pathway and networks, we may identify new molecular targets in PQ-induced pulmonary EMT. Show less
no PDF DOI: 10.1002/jbt.23681
ANGPTL4
Wei Li, Yongyi Wang, Ritai Huang +4 more · 2024 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
Coronary artery disease (CAD) is a complex disease that is influenced by environmental and genetic factors. In this study, we aimed to investigate the relationship between coding variants in lipid met Show more
Coronary artery disease (CAD) is a complex disease that is influenced by environmental and genetic factors. In this study, we aimed to investigate the relationship between coding variants in lipid metabolism-related genes and CAD in a Chinese Han population. A total of 252 individuals were recruited for this study, including 120 CAD patients and 132 healthy control individuals. Rare and common coding variants in 12 lipid metabolism-related genes (ANGPTL3, ANGPTL4, APOA1, APOA5, APOC1, APOC3, CETP, LDLR, LIPC, LPL, PCSK9 and SCARB1) were detected via next-generation sequencing (NGS)-based targeted sequencing. Associations between common variants and CAD were evaluated by Fisher's exact test. A gene-based association test of rare variants was performed by the sequence kernel association test-optimal (SKAT-O test). We found 51 rare variants and 17 common variants in this study. One common missense variant, LIPC rs6083, was significantly associated with CAD after Bonferroni correction (OR = 0.47, 95% CI = 0.29-0.76, p = 1.9 × 10 Targeted sequencing is a powerful tool for identifying rare and common variants in CAD. The common missense variant LIPC rs6083 confers protection against CAD. The clinical relevance of rare variants in CAD aetiology needs to be investigated in larger sample sizes in the future. Show less
📄 PDF DOI: 10.1186/s12872-024-03759-5
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Hongling Hu, Sheng Luo, Pinglin Lai +18 more · 2024 · Proceedings of the National Academy of Sciences of the United States of America · National Academy of Sciences · added 2026-04-24
Leptin protein was thought to be unique to leptin receptor (LepR), but the phenotypes of mice with mutation in LepR [
📄 PDF DOI: 10.1073/pnas.2310685120
ANGPTL4
Yu-Ting Kang, Wan-Jung Yang, Hsu Chih Huang +2 more · 2024 · Environmental toxicology · Wiley · added 2026-04-24
Nickel (Ni) is a human carcinogen with genotoxic and epigenotoxic effects. Environmental and occupational exposure to Ni increases the risk of cancer and chronic inflammatory diseases. Our previous fi Show more
Nickel (Ni) is a human carcinogen with genotoxic and epigenotoxic effects. Environmental and occupational exposure to Ni increases the risk of cancer and chronic inflammatory diseases. Our previous findings indicate that Ni alters gene expression through epigenetic regulation, specifically impacting E-cadherin and angiopoietin-like 4 (ANGPTL4), involved in epithelial-mesenchymal transition and migration. GST-M2, a member of the glutathione S-transferase (GST) enzyme family, plays a crucial role in cellular defense against oxidative damage and has been increasingly associated with cancer. GST-M2 overexpression inhibits lung cancer invasion and metastasis in vitro and in vivo. Hypermethylation of its promoter in cancer cells reduces gene expression, correlating with poor prognosis in non-small-cell lung cancer patients. The impact of Ni on GST-M2 remains unclear. We will investigate whether nickel exerts regulatory effects on GST-M2 through epigenetic modifications. Additionally, metformin, an antidiabetic drug, is being studied as a chemopreventive agent against nickel-induced damage. Our findings indicate that nickel chloride (NiCl Show less
no PDF DOI: 10.1002/tox.24055
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Di Ouyang, Chunying Huang, Huihua Liu +4 more · 2024 · Frontiers in neurology · Frontiers · added 2026-04-24
Migraine is a common neurological disorder that affects more than one billion people worldwide. Recent genome-wide association studies have identified 123 genetic loci associated with migraine risk. H Show more
Migraine is a common neurological disorder that affects more than one billion people worldwide. Recent genome-wide association studies have identified 123 genetic loci associated with migraine risk. However, the biological mechanisms underlying migraine and its relationships with other complex diseases remain unclear. We performed a phenome-wide association study (PheWAS) using UK Biobank data to investigate associations between migraine and 416 phenotypes. Mendelian randomization was employed using the IVW method. For loci associated with multiple diseases, pleiotropy was tested using MR-Egger. Single-cell RNA sequencing data was analyzed to profile the expression of 73 migraine susceptibility genes across brain cell types. qPCR was used to validate the expression of selected genes in microglia. PheWAS identified 15 disorders significantly associated with migraine, with one association detecting potential pleiotropy. Single-cell analysis revealed elevated expression of seven susceptibility genes (including ZEB2, RUNX1, SLC24A3, ANKDD1B, etc.) in brain glial cells. And qPCR confirmed the upregulation of these genes in LPS-treated microglia. This multimodal analysis provides novel insights into the link between migraine and other diseases. The single-cell profiling suggests the involvement of specific brain cells and molecular pathways. Validation of gene expression in microglia supports their potential role in migraine pathology. Overall, this study uncovers pleiotropic relationships and the biological underpinnings of migraine susceptibility. Show less
📄 PDF DOI: 10.3389/fneur.2024.1301208
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Qingwu Xin, Li Li, Bangzhe Zhao +7 more · 2024 · Poultry science · Elsevier · added 2026-04-24
To explore the differential regulation mechanism of heat stress on the egg production performance and egg quality of Jinding ducks, 200 Jinding ducks (360-day-old) in good health and with similar body Show more
To explore the differential regulation mechanism of heat stress on the egg production performance and egg quality of Jinding ducks, 200 Jinding ducks (360-day-old) in good health and with similar body weights and a normal appetite were selected and randomly divided into a control (normal temperature [NT]) group (20°C-25°C) and a heat stress (HS) group (32°C-36°C), with 4 replicates in each group and 25 ducks in each replicate. The pretrial period was 1 wk, and the formal trial period was 4 wk. At the end of the 4th wk, 12 duck eggs were collected from each replicate to determine egg quality. Pituitary and ovarian tissues of Jinding ducks were collected, transcriptome sequencing was performed to screen differentially expressed miRNAs and mRNAs related to high temperature and heat stress, and a competitive endogenous RNA regulatory network was constructed. The sequencing data were verified by qRT‒PCR method. The following results were obtained: (1) Compared with the NT group, the HS group had a significantly lower laying rate, total egg weight, average egg weight, total feed intake, and feed intake per duck (P < 0.01), an extremely significantly higher feed-to-egg ratio (P < 0.01), and a higher mortality rate. (2) Compared with the NT group, the HS group had an extremely significantly lower egg weight, egg yolk weight, eggshell weight, and eggshell strength (P < 0.01) and an extremely significantly lower yolk ratio and eggshell thickness (P < 0.01, P < 0.05); however, there was no significant difference in the egg shape index, Haugh unit or protein height (P > 0.05). (3) A total of 1,974 and 1,202 genes were identified in the pituitary and ovary, respectively, and there were 5 significantly differentially expressed miRNAs. The differentially expressed genes were involved in the arginine and proline metabolism pathways, ether lipid metabolism pathway, and drug metabolism-cytochrome P450 pathway, which are speculated to be related to the egg production performance of Jingding ducks under high-temperature heat stress. (4) Novel₂₂₁ may target the PRPS1 gene to participate in egg production performance; novel₁₆₈ and novel₂₈₉ may target PIGW; novel₂₈₉ may target Q3MUY2; and novel₂₈₉ and novel₂₀₈ may target PIGN or genes that may be related to high-temperature heat stress. (5) In pituitary tissue, upregulated novel₁₄₁ (center of the network) formed a regulatory network with HSPB1 and HSP30A, and downregulated novel₃₆₆ (center of the network) formed a regulatory network with the JIP1 gene. In ovarian tissue, downregulated novel₂₈₉ (center of the network) formed a regulatory network with the ZSWM7, ABI3, and K1C23 genes, novel₂₂₁ formed a regulatory network with the IGF1, BCL7B, SMC6, APOA4, and FARP2 genes, and upregulated novel₄₀ formed a regulatory network with the HA1FF10 gene. In summary, heat stress affects the production performance and egg quality of Jinding ducks by regulating the secretion of endocrine-related hormones and the release of neurotransmitters as well as the expression of miRNAs and mRNAs in pituitary and ovarian tissues. The miRNA‒mRNA regulatory network provides a theoretical basis for the molecular mechanism that regulates the stress response in pituitary and ovarian tissues, egg quality, and production performance under heat stress. Show less
📄 PDF DOI: 10.1016/j.psj.2023.103255
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Jiabao Guo, Guolin Miao, Wenxi Zhang +12 more · 2024 · Theranostics · added 2026-04-24
📄 PDF DOI: 10.7150/thno.91084
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Meng-Na Huang, Chen-Cen Wang, Ming-Sheng Ma +22 more · 2024 · Lipids in health and disease · BioMed Central · added 2026-04-24
Familial hypercholesterolemia (FH) is an inherited disorder mainly marked by increased low-density lipoprotein cholesterol (LDL-C) concentrations and a heightened risk of early-onset arteriosclerotic Show more
Familial hypercholesterolemia (FH) is an inherited disorder mainly marked by increased low-density lipoprotein cholesterol (LDL-C) concentrations and a heightened risk of early-onset arteriosclerotic cardiovascular disease (ASCVD). This study seeks to characterize the genetic spectrum and genotype‒phenotype correlations of FH in Chinese pediatric individuals. Data were gathered from individuals diagnosed with FH either clinically or genetically at multiple hospitals across mainland China from January 2016 to June 2024. In total, 140 children and adolescents (mean age of 6.00 years) with clinically and genetically diagnosed FH were enrolled in the study, with 87 distinct variants identified in the LDLR, APOB and PCSK9 genes. Among the variants, 11 variants were newly identified worldwide, with 9 classified as "pathogenic" or "likely pathogenic", and 2 classified as "variants of uncertain significance". Additionally, the 5 most common variants in the study were c.1448G > A (p.W483*), c.1879G > A (p.A627T), c.1216C > A (p.R406R), and c.1747C > T (p.H583Y) in the LDLR gene, as well as c.10579C > T (p.R3527W) in the APOB gene, accounting for 49.29% (69/140) of all patients. These variants are primarily observed in the Asian or Chinese population and are distinct from those present in Caucasian groups. In this cohort, 105 patients were diagnosed with heterozygous FH (HeFH), while 35 were diagnosed with homozygous FH (HoFH). Finally, only 28.57% of the patients (40/140) were using lipid-lowering medications with 33.33% of HoFH patients initiating treatment after the age of 8. Additionally, only 3 compound heterozygous patients (2.14%) underwent liver transplantation because of significantly high lipid levels. This study reveals the variable genotypes and phenotypes of children with FH in China and illustrates that the genotypes in the Chinese population differ from those in Caucasians, providing a valuable dataset for the clinical genetic screening of FH in China. Furthermore, the older age at diagnosis and treatment highlights the underdiagnosis and undertreatment of Chinese FH pediatric patients, suggesting that early identification should be improved through lipid or genetic screening, and that more timely and regular pharmacological treatments should be implemented. Show less
📄 PDF DOI: 10.1186/s12944-024-02406-4
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Shuhui Chen, Hao Lin, Bin Liu +4 more · 2024 · BMC immunology · BioMed Central · added 2026-04-24
Frailty is an emerging global burden of disease, characterized as an age-related clinical syndrome. Recent studies have suggested a potential link of circulating protein levels with the onset of frail Show more
Frailty is an emerging global burden of disease, characterized as an age-related clinical syndrome. Recent studies have suggested a potential link of circulating protein levels with the onset of frailty. This study aims to analyze the potential causal relationships of plasma proteins with frailty using a Mendelian Randomization (MR) study design. Associations of plasma proteins with frailty were assessed using inverse variance weighted (IVW), MR-Egger regression, weighted median, maximum-likelihood method, and MR-PRESSO test. Protein-protein interaction network construction and gene ontology functional enrichment analysis were conducted based on MR-identified target proteins. After false discovery rate (FDR) correction, MR analysis identified five plasma proteins, including BIRC2 [OR = 0.978, 95%CI (0.967-0.990)] and PSME1 [OR = 0.936, 95%CI (0.909-0.965)], as protective factors against frailty, and 49 proteins, including APOB [OR = 1.053, 95%CI (1.037-1.069)] and CYP3A4 [OR = 1.098, 95%CI (1.068-1.128)], as risk factors. Network analysis suggested BIRC2, PSME1, APOE, and CTNNB1 as key intervention targets. This study employed MR design to investigate the association of circulating plasma proteins with frailty, identified five proteins negatively associated with frailty risk and 49 proteins positively associated with frailty. Show less
📄 PDF DOI: 10.1186/s12865-024-00677-1
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Yuhui Huang, Xuehui Sun, Qingxia Huang +13 more · 2024 · Translational psychiatry · Nature · added 2026-04-24
The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites a Show more
The role of circulating metabolome in cognitive impairment is inconclusive, and whether the associations are in the severity-dependent manner remains unclear. We aimed to identify plasma metabolites associated with cognitive impairment and evaluate the added predictive capacity of metabolite biomarkers on incident cognitive impairment beyond traditional risk factors. In the Rugao Longevity and Ageing Study (RuLAS), plasma metabolome was profiled by nuclear magnetic resonance spectroscopy. Participants were classified into the cognitively normal, moderately impaired, and severely impaired groups according to their performance in two objective cognitive tests. A two-step strategy of cross-sectional discovery followed by prospective validation was applied. In the discovery stage, we included 1643 participants (age: 78.9 ± 4.5 years) and conducted multinomial logistic regression. In the validation stage, we matched 68 incident cases of cognitive impairment (moderately-to-severely impaired) during the 2-year follow-up with 204 cognitively normal controls by age and sex at a 1:3 ratio, and conducted conditional logistic regression. We identified 28 out of 78 metabolites cross-sectionally related to severely impaired cognition, among which IDL particle number, ApoB in IDL, leucine, and valine were prospectively associated with 28%, 28%, 29%, and 33% lower risk of developing cognitive impairment, respectively. Incorporating 13 metabolite biomarkers selected through Lasso regression into the traditional risk factors-based prediction model substantially improved the ability to predict incident cognitive impairment (AUROC: 0.839 vs. 0.703, P < 0.001; AUPRC: 0.705 vs. 0.405, P < 0.001). This study identified specific plasma metabolites related to cognitive impairment. Incorporation of specific metabolites substantially improved the prediction performance for cognitive impairment. Show less
📄 PDF DOI: 10.1038/s41398-024-03147-9
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Haomin Huang, Lamei Li, Anni Yang +5 more · 2024 · Frontiers in cardiovascular medicine · Frontiers · added 2026-04-24
Coronary artery disease (CAD) remains the primary cause of death worldwide, and familial hypercholesterolemia (FH) is a common disease that leads to CAD. This study aimed to explore the difference in Show more
Coronary artery disease (CAD) remains the primary cause of death worldwide, and familial hypercholesterolemia (FH) is a common disease that leads to CAD. This study aimed to explore the difference in CAD risk between FH and non-FH patients with high low-density lipoprotein cholesterol (LDL-C) levels. Individuals (≥18 years) who underwent coronary angiography (CAG) from June 2016 to September 2020 were consecutively enrolled. Participants with LDL-C levels ≥4.0 mmol/L were ultimately included in this study. For all participants, next-generation sequencing was performed with expanded gene panels including 11 genes (LDLR, APOB, PCSK9, LDLRAP1, ABCG5, ABCG8, LIPA, LPA, APOBR, LRPAP1, and STAP1). A total of 223 individuals were included in this study. According to the CAG findings, 199 CAD patients and 24 non-CAD patients were included. The proportions of FH genes, regardless of whether 3 major genes or all 11 genes were sequenced, were not significantly different between the CAD and non-CAD groups ( FH mutation did not increase the rate of CAD in individuals with an MLDL-C level ≥4.0 mmol/L. However, among CAD patients (MLDL-C level ≥4.0 mmol/L) with almost normal renal function (≥87.4 ml/min/1.73 m Show less
📄 PDF DOI: 10.3389/fcvm.2024.1434392
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Wang-Dong Xu, You-Yue Chen, Xiang Wang +2 more · 2024 · Seminars in arthritis and rheumatism · Elsevier · added 2026-04-24
The aim of this study is to develop and validate a nomogram that can assist clinicians in identifying female systemic lupus erythematosus (SLE) patients of reproductive age complicated with interstiti Show more
The aim of this study is to develop and validate a nomogram that can assist clinicians in identifying female systemic lupus erythematosus (SLE) patients of reproductive age complicated with interstitial lung disease (ILD). Clinical, laboratory data of SLE patients were first collected. Meteorological data were then gathered according to the geographical locations of the SLE patients. Diagnostic results, univariate logistic regression, elastic net regression, and multivariate logistic regression were used to screen for risk factors for female SLE patients of reproductive age complicated with ILD. A nomogram was constructed using these risk factors and was internally and externally validated through methods such as calculating the concordance index, plotting calibration curves, drawing receiver operating characteristic curves, and clinical decision curves. A total of 4798 SLE patients were included in this study, with 2488 patients in the development set and 2310 patients in the external validation set. The patients in the development set were randomly divided into a training set (N = 1742) and an internal testing set (N = 746) at a ratio of 7:3. Eight independent risk factors for ILD were identified, including APOB, APOA1, ALP, PLT, HCT, EOS-R, LYM-R, and age. The nomogram model was developed, and the areas under the receiver operating characteristic curve was 0.811 (0.748, 0.875), 0.820 (0.727,0.913), and 0.889 (0.869, 0.909) for the three sets, respectively. We established a nomogram model using easily accessible clinical and laboratory data to predict the probability of female SLE patients of reproductive age developing ILD. Show less
no PDF DOI: 10.1016/j.semarthrit.2024.152556
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Yongjiang Cheng, Jingyan Ye, Junyuan Huang +1 more · 2024 · PeerJ · added 2026-04-24
Cholestasis is characterized by the accumulation of bile in the liver or biliary system due to obstruction or impaired flow, necessitating lipid profiling to assess lipid metabolism abnormalities. Int Show more
Cholestasis is characterized by the accumulation of bile in the liver or biliary system due to obstruction or impaired flow, necessitating lipid profiling to assess lipid metabolism abnormalities. Intrahepatic cholestasis, being the most significant type of cholestasis, further complicates the assessment of lipid abnormalities. However, the accuracy of low-density lipoprotein cholesterol (LDL-C) measurement in intrahepatic cholestasis patients remains uncertain. This study aimed to evaluate the consistency of the homogeneous assay and the Friedewald formula in detecting LDL-C levels and identify factors influencing LDL-C test results in intrahepatic patients with cholestasis. Retrospective analysis of laboratory data was conducted on intrahepatic cholestatic patients. Correlations between LDL-C values obtained using the homogeneous method (LDL-C(D)) and the Friedewald formula (LDL-C(F)), as well as associations between high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A1 (ApoA1), LDL-C(D) and LDL-C(F), and apolipoprotein B (ApoB), were analyzed. Logistic regression analyses were employed to identify diagnostic indicators for inaccurate LDL-C measurements in intrahepatic cholestatic patients. Compared to patients with intrahepatic cholestasis without jaundice, the correlation between LDL-C(F) and LDL-C(D) was weaker in those with jaundice. Additionally, HDL-C exhibited a strong correlation with ApoA1 in both jaundice and non-jaundice cholestasis cases. Elevated non-HDL-C to APOB ratio (NH-C/B Ratio) levels (>4.5) were identified as a reliable predictor of inaccurate LDL-C measurements in patients with chronic intrahepatic cholestasis accompanied by jaundice. LDL-C measurement reliability is moderately weaker in patients with intrahepatic cholestasis accompanied by jaundice. Elevated levels of the NH-C/B ratio serve as a significant predictor of inaccurate LDL-C measurements in this chronic patient population, highlighting its clinical relevance for diagnostic assessments. Show less
📄 PDF DOI: 10.7717/peerj.18224
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Bohua Zhang, Yafang Huang, Jingjing Zhang +5 more · 2024 · Experimental gerontology · Elsevier · added 2026-04-24
Tamoxifen has been used in the management of breast cancer. The available evidence on the effect of tamoxifen on lipoprotein(a) and apolipoproteins is controversial. Hence, this meta-analysis of rando Show more
Tamoxifen has been used in the management of breast cancer. The available evidence on the effect of tamoxifen on lipoprotein(a) and apolipoproteins is controversial. Hence, this meta-analysis of randomized controlled trials (RCTs) was conducted to increase the quality of evidence on the effect of tamoxifen on lipoprotein(a) and apolipoproteins. Eligible RCTs published up to September 2023 were carefully selected following a comprehensive search. Thereafter, a meta-analysis was conducted using a random-effects model and the results were presented as the weighted mean difference (WMD) with a 95 % confidence interval (CI). The results from the random-effects model indicated a rise in ApoA-I (WMD: 16.24 mg/dL, 95 % CI: 5.35, 27.12, P = 0.003), and a decrease in ApoB (WMD: -9.37 mg/dL, 95 % CI: -15.16, -3.59, P = 0.001) and lipoprotein(a) (WMD: -3.24 mg/dL, 95 % CI: -5.66, -0.83, P < 0.001) concentrations following tamoxifen administration in women. Furthermore, a more pronounced decrease in ApoB (WMD: -12.86 mg/dL, 95 % CI: -19.78, -5.93, P < 0.001) and elevation in ApoA-1 levels (WMD: 51.97 mg/dL, 95 % CI: 45.89, 58.05, P < 0.001) were identified in a single study on patients with breast cancer. The current meta-analysis demonstrated an increase of ApoA-I and a decrease of ApoB and lipoprotein(a) levels after treatment with tamoxifen in women. Show less
no PDF DOI: 10.1016/j.exger.2024.112587
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S Z Huang, M Y Yu · 2024 · Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] · added 2026-04-24
Dyslipidemia stands as an autonomous peril in the realm of atherosclerotic cardiovascular maladies. Prompt identification and timely intervention in the case of dyslipidemia hold promise for substanti Show more
Dyslipidemia stands as an autonomous peril in the realm of atherosclerotic cardiovascular maladies. Prompt identification and timely intervention in the case of dyslipidemia hold promise for substantially curbing the onset and fatality rates associated with coronary heart disease. Traditional lipid surveillance metrics employed in clinical settings, such as low-density lipoprotein cholesterol, exhibit notable limitations. Conversely, lipid-derived parameters emerge as formidable contenders, demonstrating a capacity to amalgamate and quantify disparate risk factors and multifactorial etiologies inherent in a given disease. By encompassing a broader spectrum of information than singular indices, these parameters offer a more profound insight into disease progression by virtue of their grounding in the physiological intricacies of lipid metabolism. Drawing upon extant domestic and international guidelines and research, this discourse delineates and synthesizes four lipid-derived parameters with promising clinical applications: atherogenic index of plasma, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio, apolipoprotein B/A1 ratio, and lipoprotein combine index, and forwards a perspective grounded in current strides in clinical research. Show less
no PDF DOI: 10.3760/cma.j.cn112150-20240304-00183
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Chunyu Huang, Weipeng Liang, Yuying Sun · 2024 · Advances in laboratory medicine · added 2026-04-24
To investigate the role of body mass index (BMI), serum lipid profile molecules and their derivative indexes in colorectal polyps. A total of 352 individuals who underwent colonoscopy at our center we Show more
To investigate the role of body mass index (BMI), serum lipid profile molecules and their derivative indexes in colorectal polyps. A total of 352 individuals who underwent colonoscopy at our center were included in this retrospective analysis. Of these, 247 patients without evident abnormalities (control group), while 105 patients diagnosed with colorectal polyps (patient group). Serum lipid profile molecules and their derivative indexes were then compared between the two groups. The patient group exhibited significantly higher levels of total cholesterol (TC) and apolipoprotein B (ApoB) compared to the control group (p<0.05). In males, the patient group displayed elevated levels of ApoB and ApoB/ApoA1 ratio compared to the control group (p<0.05). Additionally, the triglycerides (TG) and TG/high-density lipoprotein-cholesterol (HDL-C) ratios were significantly higher in the multiple polyps group than in the single polyp group (p<0.05). Furthermore, the HDL-C and HDL-C/ApoA1 ratio levels were higher in the adenomatous polyp group when compared to the non-adenomatous polyp group (p<0.05). Multiple logistic regression analysis indicated that total cholesterol (TC), TG, low-density lipoprotein-cholesterol (LDL-C), TC/HDL-C ratio, TG/HDL-C ratio and LDL-C/HDL-C ratio were risk factors for the occurrence of colorectal polyps (p<0.05). ROC curve analyses revealed that TC, ApoB, and ApoB/ApoA1 ratio were associated with colorectal polyps. No significant difference in BMI between the two groups (p>0.05). The incidence and progression of colorectal polyps are linked to serum lipid molecules and their derivative indexes. Dyslipidemia may increase the risk of colorectal polyps, potentially leading to colorectal cancer (CRC). Show less
📄 PDF DOI: 10.1515/almed-2023-0170
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Xiangming Huang, Mengqiu Zhang, Lina Gu +9 more · 2024 · Phytotherapy research : PTR · Wiley · added 2026-04-24
Intestinal metaplasia (IM) is a premalignant condition that increases the risk for subsequent gastric cancer (GC). Traditional Chinese medicine generally plays a role in the treatment of IM, and the p Show more
Intestinal metaplasia (IM) is a premalignant condition that increases the risk for subsequent gastric cancer (GC). Traditional Chinese medicine generally plays a role in the treatment of IM, and the phytochemical naringenin used in Chinese herbal medicine has shown therapeutic potential for the treatment of gastric diseases. However, naringenin's specific effect on IM is not yet clearly understood. Therefore, this study identified potential gene targets for the treatment of IM through bioinformatics analysis and experiment validation. Two genes (MTTP and APOB) were selected as potential targets after a comparison of RNA-seq results of clinical samples, the GEO dataset (GSE78523), and naringenin-related genes from the GeneCards database. The results of both cell and animal experiments suggested that naringenin can improve the changes in the intestinal epithelial metaplasia model via MTTP/APOB expression. In summary, naringenin likely inhibits the MTTP/APOB axis and therefore inhibits IM progression. These results support the development of naringenin as an anti-IM agent and may contribute to the discovery of novel IM therapeutic targets. Show less
no PDF DOI: 10.1002/ptr.8279
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Huibin Huang, Juan Li, Tianhua Chen +5 more · 2024 · Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology · Taylor & Francis · added 2026-04-24
To analyse changes in lipid levels during the development of intrahepatic cholestasis of pregnancy (ICP) and identify new biomarkers for predicting ICP. A retrospective case-control study was conducte Show more
To analyse changes in lipid levels during the development of intrahepatic cholestasis of pregnancy (ICP) and identify new biomarkers for predicting ICP. A retrospective case-control study was conducted to analyse 473 pregnant women who underwent regular prenatal examinations and delivered at the Women and Children's Hospital, School of Medicine, Xiamen University, between June 2020 and June 2023, including 269 normal pregnancy controls and 204 pregnant women with cholestasis. Patients with ICP with gestational diabetes mellitus (GDM) have lower high-density lipoprotein (HDL) levels than in those without GDM. Total bile acid (TBA) levels were significantly higher in pregnant women with GDM than those without. The apolipoprotein A (APOA) level was lower in patients with ICP and hypothyroidism than those without hypothyroidism. TBA levels were significantly higher in pregnant women with hypothyroidism than those without. Triglyceride (TG) levels were significantly higher in patients with preeclampsia (PE) than those without. HDL and APOA levels were lower in women with ICP complicated by preterm delivery than those with normal delivery. The AUC (area under the curve) of the differential diagnosis of cholestasis of pregnancy for the APOA/APOB (apolipoprotein B) ratio was 0.727, with a sensitivity of 85.9% and specificity of 47.5%. The results suggested that dyslipidaemia is associated with an increased risk of ICP and its complications. The timely detection of blood lipid and bile acid levels can assist in the diagnosis of ICP and effectively prevent ICP and other complications. Show less
no PDF DOI: 10.1080/01443615.2024.2369929
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Su-Guo Wang, Yong-Gang Wang, Guo-Wei Qian +8 more · 2024 · Current medical science · Springer · added 2026-04-24
To investigate the serum lipid profiles of patients with localized osteosarcoma around the knee joint before and after neoadjuvant chemotherapy. After retrospectively screening the data of 742 patient Show more
To investigate the serum lipid profiles of patients with localized osteosarcoma around the knee joint before and after neoadjuvant chemotherapy. After retrospectively screening the data of 742 patients between January 2007 and July 2020, 50 patients aged 13 to 39 years with Enneking stage II disease were included in the study. Serum lipid levels, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), lipoprotein-α [Lp(a)], and apolipoprotein A1, B, and E (ApoA1, ApoB, and ApoE), and clinicopathological characteristics were collected before and after neoadjuvant chemotherapy. The mean levels of TC, TG, and ApoB were significantly increased following neoadjuvant chemotherapy (16%, 38%, and 20%, respectively, vs. pretreatment values; P<0.01). The mean levels of LDL-C and ApoE were also 19% and 16% higher, respectively (P<0.05). No correlation was found between the pretreatment lipid profile and the histologic response to chemotherapy. An increase in Lp(a) was strongly correlated with the Ki-67 index (R=0.31, P=0.023). Moreover, a trend toward longer disease-free survival (DFS) was observed in patients with decreased TG and increased LDL-C following chemotherapy, although this difference was not statistically significant (P=0.23 and P=0.24, respectively). Significant elevations in serum lipids were observed after neoadjuvant chemotherapy in patients with localized osteosarcoma. There was no prognostic significance of pretreatment serum lipid levels on histologic response to neoadjuvant chemotherapy. The scale of increase in serum Lp(a) might have a potential prognostic role in osteosarcoma. Patients with increased LDL-C or reduced TG after chemotherapy seem to exhibit a trend toward favorable DFS. Show less
📄 PDF DOI: 10.1007/s11596-024-2852-8
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Susanna Canali, Alexander W Fischer, Mychael Nguyen +15 more · 2024 · Molecular metabolism · Elsevier · added 2026-04-24
Interleukin (IL)-22 is a potential therapeutic protein for the treatment of metabolic diseases such as obesity, type 2 diabetes, and metabolic dysfunction-associated steatotic liver disease due to its Show more
Interleukin (IL)-22 is a potential therapeutic protein for the treatment of metabolic diseases such as obesity, type 2 diabetes, and metabolic dysfunction-associated steatotic liver disease due to its involvement in multiple cellular pathways and observed hepatoprotective effects. The short serum half-life of IL-22 has previously limited its use in clinical applications; however, the development of mRNA-lipid nanoparticle (LNP) technology offers a novel therapeutic approach that uses a host-generated IL-22 fusion protein. In the present study, the effects of administration of an mRNA-LNP encoding IL-22 on metabolic disease parameters was investigated in various mouse models. C57BL/6NCrl mice were used to confirm mouse serum albumin (MSA)-IL-22 protein expression prior to assessments in C57BL/6NTac and CETP/ApoB transgenic mouse models of metabolic disease. Mice were fed either regular chow or a modified amylin liver nonalcoholic steatohepatitis-inducing diet prior to receiving either LNP-encapsulated MSA-IL-22 or MSA mRNA via intravenous or intramuscular injection. Metabolic markers were monitored for the duration of the experiments, and postmortem histology assessment and analysis of metabolic gene expression pathways were performed. MSA-IL-22 was detectable for ≥8 days following administration. Improvements in body weight, lipid metabolism, glucose metabolism, and lipogenic and fibrotic marker gene expression in the liver were observed in the MSA-IL-22-treated mice, and these effects were shown to be durable. These results support the application of mRNA-encoded IL-22 as a promising treatment strategy for metabolic syndrome and associated comorbidities in human populations. Show less
📄 PDF DOI: 10.1016/j.molmet.2024.101965
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Lixuan Huang, Ying Sun, Chao Luo +5 more · 2024 · Frontiers in pharmacology · Frontiers · added 2026-04-24
Schizophrenia significantly impacts cognitive and behavioral functions and is primarily treated with second-generation antipsychotics (SGAs) such as olanzapine. Despite their efficacy, these drugs are Show more
Schizophrenia significantly impacts cognitive and behavioral functions and is primarily treated with second-generation antipsychotics (SGAs) such as olanzapine. Despite their efficacy, these drugs are linked to serious metabolic side effects which can diminish patient compliance, worsen psychiatric symptoms and increase cardiovascular disease risk. This study explores the hypothesis that SGAs affect the molecular determinants of synaptic plasticity and brain activity, particularly focusing on the lateral septum (LS) and its interactions within hypothalamic circuits that regulate feeding and energy expenditure. Utilizing functional ultrasound imaging, RNA sequencing, and weighted gene co-expression network analysis, we identified significant alterations in the functional connection between the hypothalamus and LS, along with changes in gene expression in the LS of mice following prolonged olanzapine exposure. Our analysis revealed a module closely linked to increases in body weight and adiposity, featuring genes primarily involved in lipid metabolism pathways, notably Show less
📄 PDF DOI: 10.3389/fphar.2024.1419098
APOC3
Hsiao-Chin Shen, Mei-Hung Pan, Chih-Jen Huang +7 more · 2024 · Gene · Elsevier · added 2026-04-24
Links have been reported between the airflow limitation and both metabolic syndrome (MetS) and fatty liver (FL). Additionally, associations between genetic factors and risks of MetS, FL, and airflow l Show more
Links have been reported between the airflow limitation and both metabolic syndrome (MetS) and fatty liver (FL). Additionally, associations between genetic factors and risks of MetS, FL, and airflow limitation have been identified separately in different studies. Our study aims to simultaneously explore the association between specific single nucleotide polymorphisms (SNPs) of certain genes and the risk of the three associated diseases. In this retrospective cross-sectional nationwide study, 150,709 participants from the Taiwan Biobank (TWB) were enrolled. We conducted a genotype-phenotype association analysis of nine SNPs on seven genes (ApoE-rs429358, MBOAT7-rs641738, LEPR-rs1805096, APOC3-rs2854116, APOC3-rs2854117, PPP1R3B-rs4240624, PPP1R3B-rs4841132, TM6SF2-rs58542926, and IFNL4-rs368234815) using data from the TWB1.0 and TWB2.0 genotype dataset. Participants underwent a series of assessments including questionnaires, blood examinations, abdominal ultrasounds, and spirometry examinations. MetS was associated with FL and airflow limitation. ApoE-rs429358, LEPR-rs1805096, APOC3-rs2854116, APOC3-rs2854117, PPP1R3B-rs4240624, PPP1R3B-rs4841132, and TM6SF2-rs58542926 were significantly associated with the risk of MetS. The cumulative impact of T alleles of ApoE-rs429358 and TM6SF2-rs58542926 on the risk of FL was observed (p-value for trend < 0.001). Individuals without MetS and airflow limitation carrying LEPR-rs1805096 G_G genotype exhibited a reduction in the forced expiratory volume in 1 s percentage prediction (Coefficient -35, 95 % confidence interval (CI) -69.7- -0.4), low forced vital capacity percentage prediction (Coefficient -41.6, 95 % CI -82.6- -0.6), and low vital capacity percentage prediction (Coefficient -42.2, 95 % CI -84.2- -0.1). MetS significantly correlated with FL and airflow limitation. Multiple SNPs were notably associated with MetS. Specifically, T alleles of ApoE-rs429358 and TM6SF2-rs58542926 cumulatively increased the risk of FL. LEPR-rs1805096 shows a trend-wise association with pulmonary function, which is significant in patients without MetS or airflow limitation. Show less
no PDF DOI: 10.1016/j.gene.2024.148660
APOC3
Zhican Huang, Ting Cui, Jin Yao +5 more · 2024 · PloS one · PLOS · added 2026-04-24
Past studies have demonstrated that patients diagnosed with rheumatoid arthritis (RA) often exhibit abnormal levels of lipids. Furthermore, certain lipid-modifying medications have shown effectiveness Show more
Past studies have demonstrated that patients diagnosed with rheumatoid arthritis (RA) often exhibit abnormal levels of lipids. Furthermore, certain lipid-modifying medications have shown effectiveness in alleviating clinical symptoms associated with RA. However, the current understanding of the causal relationship between lipids, lipid-modifying medications, and the risk of developing RA remains inconclusive. This study employed Mendelian randomization (MR) to investigate the causal connection between lipids, lipid-modifying drugs, and the occurrence of RA. We obtained genetic variation for lipid traits and drug targets related to lipid modification from three sources: the Global Lipids Genetics Consortium (GLGC), UK Biobank, and Nightingale Health 2020. The genetic data for RA were acquired from two comprehensive meta-analyses and the R8 of FINNGEN, respectively. These variants were employed in drug-target MR analyses to establish a causal relationship between genetically predicted lipid-modifying drug targets and the risk of RA. For suggestive lipid-modified drug targets, we conducted Summary-data-based Mendelian Randomization (SMR) analyses and using expression quantitative trait loci (eQTL) data in relevant tissues. In addition, we performed co-localization analyses to assess genetic confounders. Our analysis revealed no significant causal relationship between lipid and RA. We observed that the genetically predicted 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) -mediated low density lipoprotein cholesterol (LDL-C) (OR 0.704; 95% CI 0.56, 0.89; P = 3.43×10-3), Apolipoprotein C-III (APOC3) -mediated triglyceride (TG) (OR 0.844; 95% CI 0.77, 0.92; P = 1.50×10-4) and low density lipoprotein receptor (LDLR) -mediated LDL-C (OR 0.835; 95% CI 0.73, 0.95; P = 8.81×10-3) were significantly associated with a lowered risk of RA. while Apolipoprotein B-100 (APOB) -mediated LDL-C (OR 1.212; 95%CI 1.05,1.40; P = 9.66×10-3) was significantly associated with an increased risk of RA. Our study did not find any supporting evidence to suggest that lipids are a risk factor for RA. However, we observed significant associations between HMGCR, APOC3, LDLR, and APOB with the risk of RA. Show less
📄 PDF DOI: 10.1371/journal.pone.0298629
APOC3
Kuiyuan Huang, Shenan Huang, Ming Xiong · 2024 · Lipids in health and disease · BioMed Central · added 2026-04-24
Millions of individuals globally suffer from Inflammatory bowel diseases (IBDs). There is a dearth of large population-based investigations on lipid metabolism and IBDs, and it is unclear whether lipi Show more
Millions of individuals globally suffer from Inflammatory bowel diseases (IBDs). There is a dearth of large population-based investigations on lipid metabolism and IBDs, and it is unclear whether lipid-lowering drugs target IBDs causally. Consequently, the aim of this study was to investigate the effects of lipid-lowering medication targets on the occurrence and progression of IBDs. Among the more than 400,000 participants in the UK Biobank cohort and the more than 170,000 participants in the Global Lipids Genetics Consortium, a total of nine genes linked to lipid-lowering drug targets were obtained (ABCG5/ABCG8, APOB, APOC3, LDLR, LPL, HMGCR, NPC1L1, PCSK9, and PPARA). IBD data were acquired from de Lange et al. (patients/sample size of IBDs: 25042/59957; ulcerative colitis (UC): 12366/45,975; Crohn's disease (CD): 12194/40,266) and the FinnGen cohort (patients/total sample size of IBDs: 4420/176,899; CD: 1520/171,906; UC: 3325/173,711). All four datasets were cross-combined for validation via Mendelian randomization analysis, and potential mediating factors were explored via mediation analysis. Genetically proxied APOC3 inhibition was related to increased IBD risk (odds ratio (95% confidence interval): 0.87 (0.80-0.95); P < 0.01) and UC risk (0.83 (0.73-0.94); P < 0.01). IBD and CD risk were reduced by genetic mimicry of LDLR and LPL enhancements, respectively (odds ratioLDLR: 1.18 (1.03-1.36); P = 0.018; odds ratioCD: 1.26 (1.11-1.43); P = 2.60E-04). Genetically proxied HMGCR inhibition was associated with increased CD risk (0.68 (0.50-0.94); P = 0.018). These findings were confirmed through Mendelian analysis of the cross-combination of four separate datasets. APOC3-mediated triglyceride levels may contribute to IBDs partly through mediated triglycerides, Clostridium sensu stricto 1, Clostridiaceae 1, or the Lachnospiraceae FCS020 group. LDLR enhancement may contribute to IBDs partly through increasing Lactobacillaceae. Vigilance is required to prevent adverse effects on IBDs (UC) for patients receiving volanesorsen (an antisense oligonucleotide targeting ApoC3 mRNA) and adverse effects on CD for statin users. LPL and LDLR show promise as candidate drug targets for CD and IBD, respectively, with mechanisms that are potentially independent of their lipid-lowering effects. Show less
📄 PDF DOI: 10.1186/s12944-024-02026-y
APOC3